How Will Generative AI
Revolutionize Our Work?

Moravec’s Paradox is a principle in artificial intelligence that highlights the observation that tasks that are relatively easy for humans to perform can be quite difficult for machines, while tasks that are hard for humans can be relatively simple for machines. Previously, activities like playing chess and complex arithmetic calculations were effortlessly handled by computers, while object recognition, language understanding, and walking remained elusive for machines. However, with the emergence of deep learning, AI systems have made remarkable advancements in tackling tasks that were once considered human domain, such as image recognition and natural language processing. Although not all seemingly “easy” tasks have been conquered by AI, the advancements in deep learning over the past decade have undeniably brought machines closer to mastering human-centric tasks. This holds especially true for generative models.

Generative models represent a class of machine learning algorithms that surpass the constraints of decision boundaries, enabling the capacity to model the intrinsic properties of data distributions. As such, generative models overcome the limitations inherent in discriminative models. In contrast to discriminative models, which prioritize delineating decision boundaries between data distributions—like discerning between images of dogs and cats—generative models encapsulate the inherent structures and patterns within the data distributions themselves.

The capacity of generative models to capture the intrinsic structure of data is far from trivial. The complexities of data distributions, such as images and text, are vast and high-dimensional, encompassing rare examples that cannot be overlooked (i.e., long tails). However, recent advancements in deep learning techniques have served as a catalyst, unlocking a plethora of opportunities in this domain. Propelled by large datasets (e.g., the internet), sophisticated models (e.g., transformer blocks), increased computing resources (e.g., dedicated AI accelerators), and innovative learning techniques (e.g., reinforcement learning from human feedback), generative models are now making waves across the digital landscape.

Today, generative models astound us with their capacity to generate hyper-realistic images through technologies like those employed by Stable Diffusion [1], DALL-E [2], and Midjourney [3]. Meanwhile, their linguistic siblings, such as GPTs [4] and LLaMA [5], produce human-like text that defies expectations. Video creation is revolutionized with Video LDM [6], and speech synthesis achieves new heights of authenticity [7].

Large Language Models (LLMs) like ChatGPT are particularly impressive, demonstrating proficiency in a wide array of tasks, including text summarization, general question-answering, music composition, code writing, mathematical problem-solving, and even understanding human intentions (theory of mind) [8]. Groundbreaking innovations like Auto-GPT [9] and BabyAGI [10] push the envelope further, imbuing LLMs with self-prompting, memory capabilities, browsing, and critical reasoning. Unlike traditional chatbots, auto-GPT and BabyAGI may operate with minimal human intervention, edging us ever closer to the domain of Artificial General Intelligence (AGI).

As the rapid evolution of generative AI reshapes our world, the impact of these transformative techniques on the labor market emerges as a significant consideration. Both the positive and negative consequences merit our attention as we contemplate how generative AI will redefine our professional landscape and what this revolution means for the future of work. How will generative AI reshape our professional landscape?

The concept of machines encroaching upon our jobs is far from novel. Throughout history, innovations such as steam-powered machines, computers, and robots have simultaneously captivated and alarmed us with their potential impact on the labor market. In the near future, we may well equate AI’s role in the fourth industrial revolution to that of the steam engine in the first. Should AI progress maintain its current trajectory, its substantial influence on employment is all but certain. Consequently, identifying the occupations most vulnerable to AI disruption is crucial for adapting to and capitalizing on this technology.

OpenAI, the very organization responsible for ChatGPT, has explored the possible effects of LLMs on the U.S. labor market. They discovered that roughly 80% of jobs could have at least 10% of their tasks impacted by LLMs [11]. Moreover, 19% of jobs might experience 50% of their tasks being affected. As anticipated, the occupations facing the greatest impact are those heavily dependent on writing and computer programming. A corroborating study by Goldman Sachs reveals that approximately two-thirds of occupations are susceptible to some degree of AI disruption, with a quarter of jobs potentially having up to half their workload replaced [12]. The study also forecasts that administrative and legal jobs will be among those most significantly affected by AI [Figure 2].

In contrast to previous waves of automation, manual occupations in sectors like manufacturing, construction, agriculture, and mining are predicted to be less affected by this emerging technology. This is primarily due to the current disparity in advancement between data-driven AI and robotics. The OpenAI study also anticipates a lesser impact on scientific occupations and jobs demanding critical thinking. Nevertheless, recent research involving GPT3.5 and GPT4 has already demonstrated how LLMs could potentially streamline and expedite scientific endeavors [14].

General-purpose technologies, such as printing and the steam engine, are typified by their widespread diffusion, ongoing enhancement, and the generation of complementary innovations [11]. The aforementioned studies suggest that AI will be a general-purpose technology. Importantly, such technologies also present considerable opportunities for growth and development.

Over the course of history, employment displacement caused by automation has consistently been counterbalanced by the creation of new job opportunities. This phenomenon is attributable to the advent of new occupations that arise in response to technological innovations, which account for the lion’s share of long-term employment growth. A recent study by economist David Autor revealed that 60% of the present-day workforce is engaged in professions that did not exist in 1940. This implies that a staggering 85% of employment growth in the past 80 years can be ascribed to the creation of new positions driven by technological advancements [12].

A myriad of novel job opportunities is already making its presence felt. Examples include prompt designers who fine-tune prompts for optimal performance of AI based on LLMs, companies that create fashion designs using image generators like Stable Diffusion, and firms that boost productivity by providing real-time suggestions by LLMs for how to respond in customer support [13]. It has been estimated that as natural language processing advancements permeate businesses and society, they hold the potential to trigger a 7% (or nearly $7 trillion) surge in global GDP and elevate productivity growth by 1.5 percentage points over a decade [12].